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Record W4389748514 · doi:10.21037/atm-23-351

Cosmetic male genital surgery: a narrative review

2023· review· en· W4389748514 on OpenAlexaff
Michel Alain Danino, P. Trouilloud, Mehdi Benkhadra, Arthur David Danino, Romain Laurent

Bibliographic record

VenueAnnals of Translational Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsNarrative reviewNarrativeSex organMedicineDermatologyGeneral surgeryIntensive care medicineBiologyArtLiteratureGenetics

Abstract

fetched live from OpenAlex

Background and Objective: Through the centuries the appearance of the male genitalia has always been an important concern for men, symbolizing virility, potency and sexual contentment. Correction of perceived deficiencies and deformities of the male genitalia can be addressed by aesthetic surgery as well as the enhancement its external aspect. If the social acceptance of cosmetic surgery, particularly of women's breasts, dates from the early 1950s, male intimate cosmetic surgery emerged from the shadows about 10 years ago with a medical community still very suspicious and reproachful. The present paper aims to describe and discuss the current state of the art regarding male intimate cosmetic surgery. Methods: A narrative review of the literature was performed using publications from January 2000 to September 2022. The publications were retrieved from the PubMed database using Medical Subject Headings (MeSH) terms and keywords. The authors' goal is to narrate the aesthetic non-surgical and surgical enhancement procedures of the male apparatus. Key Content and Findings: This narrative review examines the diverse procedures associated with male genitalia aesthetics. Conclusions: Aesthetics of the male genitalia is now an unavoidable and important part of aesthetic surgery worldwide with an increasing demand. Nonsurgical and surgical techniques described in the literature should be reviewed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.383
GPT teacher head0.490
Teacher spread0.107 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Translational MedicineSame topicFemale Genital Mutilation/Cutting IssuesFrench-language works237,207